The proposed backbone model has been specifically designed to segment the suspicious items' contours while discarding the normal baggage content. In this series of experiments, we compared the proposed asymmetric encoder-decoder model's performance with popular encoder-decoder, scene parsing, and fully convolutional networks. In terms of \(\mu \) DC and \(\mu \) IoU, we report the performance results in Table REF .
We can observe that the proposed framework achieves the best extraction performance on OPIXray [1]} and SIXray [2]} dataset, leading the second-best UNet [3]} by 2.34% and 3.72%. On the GDXray [4]}, however, it lags from the FCN-8 [5]} and PSPNet [6]} by 6.54% and 5.91%, respectively. But as our model outperforms all the other architectures on the large-scale SIXray [2]} and OPIXray [1]} datasets, we chose it as a backbone for the rest of the experimentation.
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